


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE			: Tuple				   =					logging.get_logger(__name__)






@add_end_docstrings(a_  )
class _lowerCamelCase(		a_  ):
       def __init__(   self, *lowerCamelCase, **lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           super().__init__(*UpperCamelCase_, **UpperCamelCase_)
           requires_backends(self, 'vision')
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == 'tf'
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)
       def      UpperCamelCase	(   self, lowerCamelCase=None)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						{}
           if top_k is not None:
               _lowercase			:				List[Any] 						=						top_k
           return {}, {}, postprocess_params
       def __call__(   self, lowerCamelCase, **lowerCamelCase)			->  Tuple:




           """simple docstring"""

           return super().__call__(UpperCamelCase_, **UpperCamelCase_)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Dict 						=						load_image(UpperCamelCase_)
           _lowercase			:				Tuple 						=						self.image_processor(images=UpperCamelCase_, return_tensors=self.framework)
           return model_inputs
       def      UpperCamelCase	(   self, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.model(**UpperCamelCase_)
           return model_outputs




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=5)			->  Optional[int]:




           """simple docstring"""

           if top_k > self.model.config.num_labels:
               _lowercase			:				int 						=						self.model.config.num_labels

           if self.framework == "pt":
               _lowercase			:				Tuple 						=						model_outputs.logits.softmax(-1)[0]
               _lowercase						,							_lowercase			:				str 						=						probs.topk(UpperCamelCase_)
           elif self.framework == "tf":
               _lowercase			:				int 						=						stable_softmax(model_outputs.logits, axis=-1)[0]
               _lowercase			:				Dict 						=						tf.math.top_k(UpperCamelCase_, k=UpperCamelCase_)
               _lowercase						,							_lowercase			:				str 						=						topk.values.numpy(), topk.indices.numpy()
           else:
               raise ValueError(F'''Unsupported framework: {self.framework}''')

           _lowercase			:				List[str] 						=						scores.tolist()
           _lowercase			:				Optional[Any] 						=						ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase_, UpperCamelCase_)]










"""simple docstring"""
def 							A						(				__snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    limit + 1
       __magic_name__      				=    [0] * limit
       for first_term in range(1 ,     __snake_case  ):
              for n in range(__snake_case ,     __snake_case ,     __snake_case  ):
                     __magic_name__      				=    first_term + n / first_term
                     if common_difference % 4:  # d must be divisble by 4
                            continue
                     else:
                            common_difference /= 4
                            if (
                                first_term > common_difference
                                and first_term < 4 * common_difference
                            ):  # since x,y,z are positive integers
                                   frequency[n] += 1  # so z>0 and a>d ,also 4d<a

       __magic_name__      				=    sum(1 for x in frequency[1:limit] if x == 1_0  )

       return count


if __name__ == "__main__":
 print(f"""{solution() = }""")